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670 results for “Influence factors”
Hierarchy of the factors influencing the broad-scale waterbirds functional diversity gradients in temperate China
<p>Geographical gradients in species diversity have long fascinated biogeographers and ecologists. However, the extent and generality of the positive/negative effects of the important factors governing functional diversity (FD) patterns are still debated, especially for the freshwater domain. We examined lake productivity and functional richness (FRic) of waterbirds sampled from 35 lakes and reservoirs in northern China with a geographic coverage of over 5 million km2. We used structural equation modelling (SEM) to explore the causal relationships between geographic position, climate, lake productivity and waterbirds FRic. We found unambiguous altitudinal and longitudinal gradients in lake productivity and waterbirds FD, which were strongly mediated by local environmental factors. Specifically, we found 1) lake productivity increased northeast but decreased with altitude, and the observed gradients were driven by climate and nutrient availability, with 93% of variation explained in the individual SEM; 2) waterbirds FD showed similar geographic and elevational gradients.; the environmental factors which had direct and/or indirect effects on these geographic and elevational gradients included climate, lake productivity and morphology, which collectively explained more than 56% of the variation in waterbirds FD; and 3) a significant (P = 0.029) causality between lake productivity and waterbirds FD was confirmed. Nevertheless, the causality link was relatively weak in comparison with climate and lake area (standardized path coefficient was 0.65, 0.21, and 0.17 for climate, area, and productivity, respectively). Through articulating the dominant causality paths, our results could contribute to the mechanistic explanations underlying the observed broad–scale biodiversity gradients.</p>
Determining Factors Influencing Pre-Service ELT Students' Behavioral intention to Use ChatGPT in Türkiye
<p>This dataset is collected for the study entitled: " Determining Factors Influencing Pre-service ELT Students's Behavioral intention to Use Chat GPT in Türkiye." by Grassini & Köse collected during February & March, 2024. </p> <p> </p>
Connecting the Digital Bridges Factors Influencing Farmers' Attitude towards e-Governance
<p><span>The e-governance streamlines the delivery of government services to the farmers, such as agricultural subsidies, credit facilities, insurance schemes, extension services and other citizen charters. The objective of this study was to determine the factors influencing the attitude of farmers towards e-governance. The scale used for attitude measurement was standardized through Judge-rating and pre-testing and forty-one statements were used to assess the attitude of the farmers on a Likert scale. One hundred twenty-five (125) farmers from Phulbari upazila in Dinajpur district of Bangladesh were selected for data collection using a multi-stage random sampling procedure through structured interview method.<span> </span>The findings revealed that a significant proportion (68.0 percent) of the respondents held a moderately to highly favourable attitude towards e-governance. Although seven socio-economic factors had significant relationship with farmers’ attitude towards e-governance; the key influencing factors identification through multiple linear regression and stepwise regression indicated that knowledge of e-governance, educational qualification, age, and organizational participation were the most important. Thus, to enhance farmer engagement and trust in e-governance, policy formulation should be aimed at improving farmers’ knowledge of e-governance involves a multifaceted approach that addresses their specific needs and challenges need to be addressed.</span></p>
Data for: Unraveling the influence of essential climatic factors on the number of tones through an extensive database of languages in China
Open the record for dataset details and reuse information.
Research on Green Total-Factor Productivity of Water Resources and Its Influencing Factors in China
<p>These files include the data used to calculate green total-factor productivity of water resources and data of its certain influencing factors. </p>
Unraveling the influence of essential climatic factors on the number of tones through an extensive database of languages in China
<h2><strong>Code</strong></h2> <ul> <li><strong>01TextGrid.</strong><strong>p</strong><strong>raat<br></strong>Segment and label the sound files in all folders under the directory</li> <li><strong>02Extract voice quality data.praat<br></strong>Extract voice quality parameters, including jitter, shimmer, HNR, CPP, H1-H2, H1-A1, H1-A2, and H1-A3</li> <li><strong>03Extract pitch data.praat<br></strong>Extract pitch data, including maximum, minimum, range, mean, upper quartile, lower quartile, pitch inter-quartile range, and median absolute deviation</li> <li><strong>04Correlation Analysis and Mantel Test.R<br></strong>Correlation tests between different variables and create correlation plots.</li> <li><strong>05GAMM_Voice quality~Climate factors.R<br></strong>Examine the relationship between climate factors and voice quality</li> <li><strong>06GAMM_Tone ~ Voice quality.R<br></strong>Examine the relationship between voice quality and the number of tones</li> <li><strong>07GAMM_Tone~Climate factors.R<strong><br></strong></strong>Examine the relationship between climate factors and the number of tones</li> <li><strong>08GAMM_Pitch~Climate factors.R<br></strong>Examine the relationship between pitch variation, the number of tones, and climate factors</li> </ul> <h2><strong>Data</strong></h2> <p>All extracted data files are in the data folder.</p> <ul> <li><strong>1525dataset.csv<br></strong>The file includes data for 1,525 language varieties with the following information: geographic location names (column A), linguistic classification and ASJP name information (columns B-E), longitude and latitude and information (columns F-G), number of tones (column H), Pitch information (columns I-J), voice quality information (columns K-R), climate information (columns S-X)</li> <li><strong>Geographical distance.csv<br></strong>The geographic distances between 1,525 language varieties were calculated using the Delaunay-Dijkstra method</li> <li><strong>Language distance.csv<br></strong>The language distances between 1,525 language varieties were calculated using the ASJP method.</li> <li><strong>S</strong><strong>pecifichumiditydif</strong><strong>.csv<br></strong>Specific humidity difference dataset for the locations of 1,525 language varieties</li> <li><strong>Tonedif.csv<br></strong>Tone difference dataset among 1,525 language varieties</li> <li><strong>Voice quality data extracted using different methods.csv<br></strong>Voice quality data for 1,115 dialectal variants, analyzed at both the lexical level and the vowel "a" level. Columns B–I present voice quality parameters extracted from the vowel, while columns J–Q provide data extracted from the lexical items.</li> </ul>
SUSTAINABLE FINANCING AS A FACTOR INFLUENCING ECO-FRIENDLY BUSINESS TRANSFORMATION: EXISTING CHALLENGES AND POTENTIAL SOLUTIONS
<p>This study underscores the slow adoption of eco-friendly practices, particularly in high-emission sectors ("brown" businesses), and emphasizes the role of sustainable financing in implementation of environmentally sustainable business practices, with a particular interest in such types of corporate financing as sustainability-linked loans and a relatively new type of financing provided to companies with core business falling under eligible green criteria (loans for green businesses). The purpose was to identify existing challenges preventing promotion of these two types of sustainable financing and borrowers’ sustainable transformation, and propose potential solutions addressing these challenges. The analysis was performed based on a vast literature review. The study revealed the existence of such challenges as borrowers’ profit-driven motives, lack in regulatory transparency, concerns of financial institutions about their profitability and risk of greenwashing, and insufficient governmental support. To address these challenges, regulatory measures are recommended to incentivize financial institutions to offer reduced interest rates on loans for green business and sustainability-linked loans, with governments compensating such losses through imposing higher taxes on “brown” businesses. The study also proposes a potential action plan on amending the regulatory framework with the main focus on the need to develop respective changes with the involvement of all relevant stakeholders. It also addresses the need of establishing mandatory criteria for recognizing businesses as green to qualify for green business loans, and obligatory KPIs for each industry outlining clear targets for improving a company's sustainability profile to be eligible for sustainability-linked loans. These obligatory criteria and KPIs may be adjusted by financial institutions depending on aspects being material for the borrower. The borrower’s compliance with criteria set for green business loans should be regularly reported and verified by an expert company, similarly to the existing rules for sustainability-linked loans. Further, the study proposes incorporating a regulatory body to oversee compliance of financial institutions with these regulations and introducing penalties for their violations, and performing monitoring and adaptation of a regulatory framework, if necessary. The suggested amendments, being subject to a comprehensive feasibility study, are intended to promote the considered types of sustainable financing and boost transit to eco-friendly business practices.</p>
Examining the Factors Influencing the Website Continuous Actual Usage of SME Owners in Indonesia
<p><strong><span>The technologies encourage SMEs to expand their market. Websites have developed into important tools for ensuring the continued existence of SMEs through customer loyalty. The research problem is that SME owners have not continued to improve their websites due to factors that influence continuing intention to use the website, such as cost, facilities, etc. This study aims to examine the factors that influence SME owners' decisions to continue using websites for their businesses. A purposive sampling strategy is used to determine the size of this study sample. It also used quantitative techniques and data collecting via a Google Form survey, which included 225 of 250 SME owner respondents from the JABODETABEK area of Indonesia. The data, collected between March and June 2024, is processed using SmartPLS-SEM. The research models used are Technology Acceptance Model and Technology Organization Environment by analyzing eight variables: Relative Advantage, Complexity, Perceived Cost, Facilitating Conditions, Security Concern, Perceived Trust, Continuous Intention to Use, and Continuous Actual Usage.<span> </span>There are eight hypothesis that are significant, Relative Advantage on Continuous Intention to Use, Complexity on Continuous Intention to Use, Perceived Cost on Continuous Intention to Use, Facilitating Conditions on Continuous Intention to Use, Facilitating Conditions on Perceived Trust, Security Concern on Perceived Trust, Perceived Trust on Continuous Actual Usage, and Continuous Intention to Use on Continuous Actual Usage. More research is needed to determine whether this is an effective strategy to improve the technological digitalization of SMEs in Indonesia</span></strong></p>
Socio-Economic Factors Influencing Biogas Technology Uptake among Rural Households in Kuresoi South Sub-County, Nakuru County
<p>Biogas technology presents an alternative sustainable energy source that offers an opportunity to transform energy security, environmental sustainability, and reduction in greenhouse gas emissions. The research study explores the socioeconomic factors affecting biogas technology uptake among rural households in Kuresoi South Sub-County, Nakuru County. This is a descriptive study design based on the use of both primary and secondary data sources. The data collection covered 155 respondents through the use of questionnaires, focus group discussions, key informant interviews and observations. Selection of the respondents was done by using systematic random sampling, while data analysis was done using descriptive statistics, chi-square tests, and cross-tabulation supported by SPSS version 26. Results indicated that despite high levels of awareness, the adoption of biogas technology was low, with firewood remaining the primary source of energy in 68% of the households. Fixed dome and tubular were the biogas digester types in use, since they are relatively cheaper and more durable; however, economic factors, mainly household income, were the main determinant of uptake. The chi-square results indicated that there was a significant relationship between household income and uptake of biogas, χ² = 9.531, p = 0.048, implying that the poorer a household is, the greater the financial barrier to the technology. Level of education, too had a say in energy adoption; education and energy choice had a strong association-since χ² = 12.814, p = 0.002-which depicted that more educated households were more likely to adopt the technology. The gender factor is insignificant in influencing energy choices, underlining a proof from the fact that χ² = 2.119, p = 0.346, where broader socio-economic factors played a much greater role in decisions. This study also revealed out that radio was the effective channel for knowledge sharing and information dissemination related to biogas technology. On the other hand, partial understanding of the technical aspects has acted as a big barrier to the better diffusion of this technology. In conclusion, income levels and education are two main factors affecting the uptake of biogas technology. Enhanced education, targeted financial support and better outreach strategies go toward increasing adoption rates and supporting transitions to sustainable energy in rural areas.</p>
Data from: Environmental factors influence both abundance and genetic diversity in a widespread bird species
Genetic diversity is one of the key evolutionary variables that correlate with population size, being of critical importance for population viability and the persistence of species. Genetic diversity can also have important ecological consequences within populations, and in turn, ecological factors may drive patterns of genetic diversity. However, the relationship between the genetic diversity of a population and how this interacts with ecological processes has so far only been investigated in a few studies. Here, we investigate the link between ecological factors, local population size, and allelic diversity, using a field study of a common bird species, the house sparrow (Passer domesticus). We studied sparrows outside the breeding season in a confined small valley dominated by dispersed farms and small-scale agriculture in southern France. Population surveys at 36 locations revealed that sparrows were more abundant in locations with high food availability. We then captured and genotyped 891 house sparrows at 10 microsatellite loci from a subset of these locations (N = 12). Population genetic analyses revealed weak genetic structure, where each locality represented a distinct substructure within the study area. We found that food availability was the main factor among others tested to influence the genetic structure between locations. These results suggest that ecological factors can have strong impacts on both population size per se and intrapopulation genetic variation even at a small scale. On a more general level, our data indicate that a patchy environment and low dispersal rate can result in fine-scale patterns of genetic diversity. Given the importance of genetic diversity for population viability, combining ecological and genetic data can help to identify factors limiting population size and determine the conservation potential of populations.
Figure 2 in The behaviour of orientation of openings of burrows by Liolaemus lutzae (Squamata: Liolaemidae): is it influenced by environmental factors?
Figure 2. (Above) Direction of the openings of retreats (n = 59, in degrees) dug by Liolaemus lutzae, and (below) terrain slope direction (n = 45, in degrees) in which the retreats were dug at the restinga of the Parque Natural Municipal de Grumari, municipality of Rio de Janeiro, Brazil. The arrows represent the mean vector (µ) and the mean vector length (r) (see Table 2).
Figure 1 in The behaviour of orientation of openings of burrows by Liolaemus lutzae (Squamata: Liolaemidae): is it influenced by environmental factors?
Figure 1. (Above) Direction of the openings of retreats (n = 132, in degrees) dug by Liolaemus lutzae, and (below) terrain slope direction (n = 115, in degrees) in which the retreats were constructed at the restinga of the Reserva Ecológica Estadual de Jacarepiá, municipality of Saquarema, Brazil. The arrows represent the mean vector (µ) and the mean vector length (r) (see Table 1).
Figure 4 in Factors influencing anuran distribution in coastal dune wetlands in southern Brazil
Figure 4. Canonical correspondence analysis ordination biplot (CCA) with tadpoles species composition related to the studied wetlands and structural complexity descriptors. First axis is horizontal, second axis vertical. O = wetlands, Hp, Hypsiboas pulchellus; Lg, Leptodactylus gracilis; Lo, L. ocellatus; Pb, Physalaemus biligonigerus; Pg, Physalaemus gracilis; Pm, Pseudis minuta; Ra, Rhinella arenarum; Rd, R. dorbignyi. Variables: A, wetland area; CV, vegetation cover; ME, emergent macrophytes; MF, floating macrophytes; MS, months of drought.
Figure 3 in Factors influencing anuran distribution in coastal dune wetlands in southern Brazil
Figure 3. Canonical correspondence analysis ordination biplot (CCA) with adult anuran species composition related to the studied wetlands and structural complexity descriptors. First axis is horizontal, second axis vertical. O, wetlands; Hp, Hypsiboas pulchellus; Lg, Leptodactylus gracilis; Lo, L. ocellatus, Om, Odontophrynus maisuma; Pb, Physalaemus biligonigerus; Pg, Physalaemus gracilis; Pm, Pseudis minuta; Pf, Pseudopaludicola falcipes; Ra, Rhinella arenarum. Variables: A, wetland area; ME, emergent macrophytes; MP, macroinvertebrate predators; MS, months of drought.
Figure 2 in Factors influencing anuran distribution in coastal dune wetlands in southern Brazil
Figure 2. Mean anuran abundance of coastal dune wetlands in southern Brazil, from October 2007 to August 2008, considering tadpoles (A) and adults (B): P, permanent; TL, long-term temporary; TS, short-term temporary.
Figure 1 in Factors influencing anuran distribution in coastal dune wetlands in southern Brazil
Figure 1. Mean anuran richness of coastal dune wetlands in southern Brazil, from October 2007 to August 2008, considering tadpoles (A) and adults (B): P, permanent; TL, long-term temporary; TS, short-term temporary.
Figure 9. Phylogeny showing a in Evolution of molar shape in didelphid marsupials (Marsupialia: Didelphidae): analysis of the influence of ecological factors and phylogenetic legacy
Figure 9. Phylogeny showing a summary of the optimization for the third lower molar (m3). Numbers on the branches indicate node number. Taxon names and nodes in bold indicate the optimizations being shown. Deformation grids show the changes with respect to the previous node.
Figure 7 in Evolution of molar shape in didelphid marsupials (Marsupialia: Didelphidae): analysis of the influence of ecological factors and phylogenetic legacy
Figure 7. Scatter plots resulting from the between-group PCA of the third upper molar (M3), summarizing differences between the five diet categories. White squares with the Roman numeral of each diet category represent the centroid of the distribution for that category. Deformation grids show the extreme shape of each PC.
Figure 1 in Evolution of molar shape in didelphid marsupials (Marsupialia: Didelphidae): analysis of the influence of ecological factors and phylogenetic legacy
Figure 1. Occlusal views of the third upper (A, B) and lower (C, D) didelphid molars. A and C, molars of Didelphis albiventris showing the landmarks and semilandmarks used. B and D, didelphid molars illustrating features of crown morphology discussed in the text. Squares, landmarks; circles, semilandmarks. See text for a detailed description of landmarks. Abbreviations: ac, anterior cingulum (light grey shading); cc, centrocrista; co, cristid obliqua; ect, ectoflexus; Ent, entoconid; ento, entocristid; Hyp, hypoconid; Hypd, hypoconulid; Me, metacone; Med, metaconid; meta, metastylar corner (grey shading); Pa, paracone; Pacr, paracristid; Pad, paraconid; para, parastylar corner (dark grey shading); postcd, postcristid; prePa, preparacrista; Pr, protocone; Prcr, protocristid; Prd, protoconid; prePr, preprotocrista; posMe, metacrista; posPr, postprotocrista; StA, stylar cusp A; StB, stylar cusp B; StC, stylar cusp C; StD, stylar cusp D; StE, stylar cusp E; Ta: talonid; Tri: trigonid.
Figure 3 in Evolution of molar shape in didelphid marsupials (Marsupialia: Didelphidae): analysis of the influence of ecological factors and phylogenetic legacy
Figure 3. First upper molar (M1) shape variation along the first two principal components (PC) from the PCA of the Procrustes coordinates, showing the distribution of taxonomic groups. Deformation grids show the extreme shape of each PC.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.